plackett-burmandesignppt.pptx

medium optimization

1. Medium Optimization

2. Introduction Fermentation industry require particular product from given organisms. Only particular product is not important but it should be produce in large quantity. For the production of huge amount of particular product, either medium formulation is proper or there should be improvement in organism .

3. Introduction P r oces s of o p ti m i z a ti o n of media is d o n e b e f o r e the m edia preparation to get maximum yield at industrial level P r oces s of o p timi z a tion o f media sh o ul d b e t a r g e t orie n t ed means either for biomass production or for desire production On sm a ll s c ale it is ea s y t o d e vise a medium c o n t ain i n g pu r e compounds But i n c ase of l a r g e s c ale p r oces s f o r s a ti s f ac t o r y g r owt h of microorganisms it can be unsuitable. 3

4. Medium optimization is a process where components of medium or different conditions either varied in concentration or changed so that we can get better growth of the organisms for high productivity. Different combinations and sequences of process conditions need to investigate to determine the growth conditions, which produce the biomass with the physiological state best, constituted for product formation. There may be a sequence of phases each with a specific set of optimal conditions. Introduction

5. The optimization of a medium should meet the following seven criteria: Produce maximum yield of product or biomass per gram of substrate used Produce the maximum concentration of product or biomass Permit the maximum rate of product formation Give the minimum yield of undesired products Has consistent quality Be readily available throughout the year It will cause minimal problems during media making and sterilization It will cause minimal problems in other aspects of the production process particularly in aeration and agitation, extraction, purification and waste treatment. 4

6. Methods of optimization of media 1. Classical Method 2. Statistical methods 5

7. OPTIMIZATION The term Optimize is defined as “to make perfect”. It is used in UPSTREAM for the formulation of MEDIA. It is the process of finding the best way of using the existing resources. The factors that influence the YIELD is considered. Optimization by means of an experimental design helps in shortening the experimenting time. The design of experiments (DOE) is a structured, organized method used to determine the relationship between the factors affecting a process and the output of that process. Statistical DOE refers to the process of planning the experiment in such a way that appropriate data can be collected and analyzed statistically.

8. MEDIA OPTIMIZATION Classical Method: The process of media optimization can be performed by classical method of changing one independent variable (Nutrient, antifoam, pH, temperature etc.). Each possible combination of independent variable at appropriate levels should require a large number of experiments – x n where, x – number of levels, n – number of variables. This may be quite appropriate for 3 variables at 2 concentrations [ 2 3 ]. But not suitable for 6 nutrients at 3 concentrations [ 3 6 ]. By the above method, totally 729 trials will be required. Industrially, the aim is to perform minimum number of experiments to determine optimum conditions.

9. OPTIMIZATION – STATISTICAL METHODS PLACKETT-BURMAN DESIGN ANALYSIS OF VARIANCE (ANOVA) CENTRAL COMPOSITE DESIGN (CCD) RESPONSE SURFACE METHODOLOGY (RSM)

10. 7 The Plackett-Burman Design When more than five independent variables are to be investigated, the Plackett-Burman design may be used to find the most important variables in a system, which are then optimized in further studies This technique allows for the evaluation of X-I variables by X experiments X must be a multiple of 4, e.g. 8, 12, 16, 20, 24, etc. Factors not assigned to a variable or factors which do not have any effect can be designated as a dummy variable Dummy variable can be used to know the variance of an effect (experimental error).

11. Table 1: Plackett-Burman design for seven variables (A -G) at high and low levels in which two factors, E and G, are designated as 'dummy' variables. (From Principles of Fermentation Technology,- Peter F. Stanbury, Allen Whitaker, Stephen J. Hall, Second Edition) 8

12. Horizontal row represents a trial and each vertical column represents the H (high) and L (low) values of one variable in all the trials This design (Table 4.16) requires that the frequency of each level of a variable in a given column should be equal and that in each test (horizontal row) the number of high and low variables should be equal. Consider the variable A; for the trials in which A is high, B is high in two of the trials and low in the other two. Similarly, C will be high in two trials and low in two, as will all the remaining variables. For those trials in which A is low, B will be high two times and low two times. This will also apply to all the other variables. 9

13. The effects of the dummy variables are calculated in the same way as the effects of the experimental variables. If there are no interactions and no errors in measuring the response, the effect shown by a dummy variable should be O. This procedure will identify the important variables and allow them to be ranked in order of importance to decide which to investigate in a more detailed study to determine the optimum values to use

14. 10 Table 2: Analysis of the yields shown in Table 1

15. The stages in analysing the data (Tables 4.16 and 4.17) using Nelson's (1982) example are as follows: 1. Determining the difference between the average of the H (high) and L (low) responses for each independent and dummy variable. Difference = ΣA (H) – ΣA (L) The effect of an independent variable on the response is the difference between the average response for the four experiments at the high level and the average value for four experiments at the low level. Thus the effect of 11

16. 12 2. To estimate the mean square of each variable (the variance of effect). For A the mean square will be = 3. The e x peri m ental er r or can b e ca l cula t ed b y a v er a ging the mean squares of the dummy effects of E and G. Thus, the mean square for error =

17. The final stage is to identify the factors which are showing large effects. In the example this was done using an F-test for Factor mean square. Error mean square. When Probability Tables are examined it is found that Factors A, B and F show large effects which are very significant. Whereas C shows a very low effect which is not significant and D shows no effect. A, B and F have been identified as the most important factors.

18. The next stage would then be the optimization of the concentration of each factor. This may be done using response optimization techniques which were introduced by and Wilson (1951). Hendrix (1980) has given a very readable account of this technique and the way which it may be applied. Response surfaces are similar to contour plots or topographical maps. Whilst topographical maps show lines of constant elevation, contour plots show lines of constant value. Thus, the contours of a response surface optimization plot show lines of identical response. In this context, response means the result of an experiment carried out at particular values of the variables being investigated. Response surface methodology

19. To statistically analyze a fermentation process, the response surface methodology (RSM) can be used to explore the interactions between one or more variables (process parameters). The concept of using RSM is to perform a limited number of designed experiments to obtain an optimized response (maximum yield). RSM can be employed to maximize the biomass production by optimizing the process parameters. A second-degree polynomial equation can be used for evaluation. This method is versatile to implement even when little is known about the fermentation process. In contrast to conventional methods, the interaction between the process parameters can be determined by statistical techniques.

20. ANOVA ANalysis Of VAriance (ANOVA) is used to determine if there is any significant difference between the means of groups of data. In statistical analysis, ANOVA is based on the design of experiment. ANOVA is also applied to evaluate the response data using a statistical model. Ex.: Effect of antibiotics on various bacterial species. Typically, a one-way ANOVA is used to test the differences among at least three groups.

21. DOE DOE (design of experiments) helps to investigate the effects of input variables (factors) on an output variable (response) at the same time. These experiments consist of a series of runs (tests), in which, purposeful changes are made to the input variables. Data are collected at each run. The process conditions and product components that affect the quality is identified. The factors which yield optimized results were determined.

22. CENTRAL COMPOSITE DESIGN A central composite design is an experimental design used in RSM. A second order (quadratic) model for the response variable can be built without using a 3-level factorial experiment. The CCD method has 3 sets of experimental runs: A factorial design with factors having two levels; A set of center points , experimental runs whose values of each factor are the medians of the values used in the factorial portion. This point is often replicated in order to improve the precision of the experiment; A set of axial points , experimental runs identical to the centre points except for one factor, which will take on values both below and above the median of the two factorial levels, and typically both outside their range.

23. Coded variables (-1, +1) are often used for constructing the design. After the designed experiment is performed, a linear regression equation is used to obtain results. For EX., in a study, a central composite design was employed to investigate the effect of critical parameters of pretreatment of rice straw including temperature, time, and ethanol concentration. The residual solid, lignin recovery, and hydrogen yield were selected as the response variables or yield.

24. Stanbur y , Peter F ., Allan Whitake r , and Stephen J . Hall. Principles of fermentation technology . Elsevier, 2013. 14 5. Reference